Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Breast Cancer Gene Expression Data from Frankfurt Series

ABSTRACT: Pooling of microarray datasets seems to be a reasonable approach to increase sample size when a heterogeneous disease like breast cancer is concerned. Different methods for the adaption of datasets have been used in the literature. We have analyzed influences of these strategies using a pool of 3,030 Affymetrix U133A microarrays from breast cancer samples. We present data on the resulting concordance with biochemical assays of well known parameters and highlight critical pitfalls. We further propose a method for the inference of cutoff values directly from the data without prior knowledge of the true result. The cutoffs derived by this method displayed high specificity and sensitivity. Markers with a bimodal distribution like ER, PgR, and HER2 discriminate different biological subtypes of disease with distinct clinical courses. In contrast, markers displaying a continuous distribution like proliferation markers as Ki67 rather describe the composition of the mixture of cells in the tumor. Fresh frozen surgical biopsy samples from consecutive patients were analyzed on Affymetrix HGU133A

ORGANISM(S): Homo sapiens

SUBMITTER: Thomas Karn

PROVIDER: E-GEOD-4611 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Data-driven derivation of cutoffs from a pool of 3,030 Affymetrix arrays to stratify distinct clinical types of breast cancer.

Karn Thomas T Metzler Dirk D Ruckhäberle Eugen E Hanker Lars L Gätje Regine R Solbach Christine C Ahr Andre A Schmidt Marcus M Holtrich Uwe U Kaufmann Manfred M Rody Achim A

Breast cancer research and treatment 20090520 3

Pooling of microarray datasets seems to be a reasonable approach to increase sample size when a heterogeneous disease like breast cancer is concerned. Different methods for the adaption of datasets have been used in the literature. We have analyzed influences of these strategies using a pool of 3,030 Affymetrix U133A microarrays from breast cancer samples. We present data on the resulting concordance with biochemical assays of well known parameters and highlight critical pitfalls. We further pro ...[more]

PMID: 19455418

Publication: 1/5

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Project description:Abstract Introduction Response rates to chemotherapy remain highly variable in breast cancer patients. We set out to identify genes associated with chemotherapy resistance. We analyzed what is currently the largest single-institute set of gene expression profiles derived from breast cancers prior to a single neoadjuvant chemotherapy regimen (dose-dense doxorubicine and cyclophophamide). Methods We collected, gene expression-profiled and analyzed 178 HER2-negative breast tumor biopsies (M-bM-^@M-^XNKI datasetM-bM-^@M-^Y). We employed a recently developed approach for detecting imbalanced differential signal (DIDS) in order to identify markers of resistance to treatment. In contrast to traditional methods, DIDS is able to identify markers that show aberrant expression in only a small subgroup of the non-responder samples. Results We found a number of markers of resistance to anthracycline-based chemotherapy. We validated our findings by the analysis of three external datasets, which contained 456 HER2-negative samples in total. Since these external sets included patients who received differing treatment regimens we could only validate markers of general chemotherapy resistance. There was a highly significant overlap in the markers identified in the NKI dataset and the other three datasets. Five resistance markers, SERPINA6, BEX1, AGTR1, SLC26A3, and LAPTM4B, were identified in three of the four datasets (p-value overlap <1e-6). These five genes identified resistant tumors that could not have been identified by merely taking ER-status or proliferation into account. Conclusion The identification of these genes might lead to a better understanding of the mechanisms involved in (clinically) observed chemotherapy resistance and could possibly assist in the recognition of breast cancers in which chemotherapy does not contribute to response or survival. We collected, gene expression profiled and analyzed 178 HER2-negative breast tumor biopsies, obtained from patients scheduled to undergo neoadjuvant therapy.

Dataset Information

Breast Cancer Gene Expression Data from Frankfurt Series

Publications

Data-driven derivation of cutoffs from a pool of 3,030 Affymetrix arrays to stratify distinct clinical types of breast cancer.

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